The AI/ML & Generative AI Mastery Program is a comprehensive, industry-focused online course designed to take learners from foundational concepts to advanced real-world applications in Artificial Intelligence, Machine Learning, Deep Learning, and modern Generative AI systems. Structured as an intensive 6-week guided learning journey, this program blends theoretical clarity, hands-on implementation, and practical project development.

This course is ideal for students, working professionals, aspiring data scientists, and tech enthusiasts who want to build strong expertise in AI technologies and gain job-ready skills. It provides a complete roadmap covering core machine learning algorithms, deep learning architectures, modern NLP systems, LLMs, MLOps practices, and deployment techniques.

By the end of the program, learners will not only understand AI concepts but will also be able to design, build, deploy, and explain production-level machine learning and generative AI solutions.

AI/ML Mastery Course – 6 Weeks Curriculum

WEEK 1: Foundations + Mathematical Intuition (ML Core)

Day 1: AI/ML Landscape

• AI vs ML vs Deep Learning

• Supervised vs Unsupervised vs Reinforcement Learning

• Real-world industry use cases

• End-to-end ML pipeline

Day 2: Math for ML (Practical Understanding)

• Linear Algebra (vectors, matrices, eigenvalues intuition)

• Calculus (gradients, partial derivatives)

• Probability & Statistics (Bayes theorem, distributions)

Day 3: Data Preprocessing Mastery

• Missing values handling

• Encoding techniques

• Feature scaling

• Feature engineering techniques

• Handling imbalanced datasets (SMOTE)

Day 4: Regression Algorithms

• Linear Regression (assumptions + math)

• Regularization (Ridge, Lasso)

• Evaluation metrics (MAE, MSE, RMSE, R²)

Day 5: Classification Algorithms

• Logistic Regression

• KNN

• Naive Bayes

• Evaluation metrics (Precision, Recall, F1, ROC-AUC)

🎯 Mini Project: Customer churn prediction

WEEK 2: Tree-Based Models & Model Optimization

Day 1: Decision Trees

• Entropy & Information Gain

• Gini Index

• Overfitting control

Day 2: Ensemble Learning

• Random Forest

• Bagging vs Boosting

Day 3: Boosting Algorithms

• AdaBoost

• Gradient Boosting

• XGBoost

• LightGBM

• CatBoost

Day 4: Hyperparameter Tuning

• GridSearchCV

• RandomSearch

• Bayesian Optimization

• Cross-validation strategies

Day 5: Feature Selection & Explainability

• SHAP

• LIME

• Feature importance

• Model interpretability

🎯 Mini Project: Credit risk modeling system

WEEK 3: Deep Learning & Neural Networks

Day 1: Neural Network Basics

• Perceptron

• Activation functions

• Backpropagation

• Vanishing gradient problem

Day 2: ANN Implementation (TensorFlow / PyTorch)

• Building from scratch

• Optimizers (SGD, Adam)

• Regularization & Dropout

Day 3: CNN (Computer Vision)

• Convolutions

• Pooling

• Transfer learning

Day 4: RNN, LSTM, GRU

• Sequence modeling

• Time series forecasting

• NLP intro

Day 5: Transformers Basics

• Attention mechanism

• Self-attention

• Encoder-decoder architecture

🎯 Mini Project: Image classification or sentiment analysis

WEEK 4: NLP + Generative AI (Modern Industry Focus)

Day 1: NLP Pipeline

• Text preprocessing

• TF-IDF

• Word embeddings (Word2Vec, GloVe)

Day 2: Transformers & LLMs

• BERT

• GPT architecture

• Fine-tuning vs Prompt engineering

Day 3: RAG Systems

• Embeddings

• Vector databases (FAISS)

• Retrieval pipelines

Day 4: Agentic AI Systems

• Tool calling

• Memory systems

• Multi-agent architecture

Day 5: LLM Evaluation

• Hallucination detection

• RAG evaluation metrics

• Guardrails & safety

🎯 Mini Project: Build a domain-specific AI chatbot

WEEK 5: MLOps & Production ML

Day 1: Model Deployment

• Flask / FastAPI

• REST APIs

• Docker basics

Day 2: Cloud ML

• AWS / GCP ML services overview

• Model hosting

• CI/CD for ML

Day 3: ML Monitoring

• Data drift

• Model drift

• Retraining pipelines

Day 4: ML System Design

• Designing scalable ML systems

• Batch vs Real-time systems

• Architecture discussions

Day 5: Capstone Planning

• Project discussion

• Architecture design

• Review & improvements

WEEK 6: Capstone + Interview Mastery

Capstone Options (Choose One)

1. End-to-end ML pipeline with deployment

2. GenAI RAG system

3. Fraud detection real-time pipeline

4. Recommendation system

Interview Preparation

• ML theory questions

• System design questions

• GenAI interview Q&A

• Case studies

• Resume optimization guidance

Bonus Modules (Optional Advanced)

• Reinforcement Learning (Q-learning, Policy Gradient)

• Diffusion Models

• Multi-modal AI

• Fine-tuning LLM with LoRA

• Building SaaS AI product

Deliverables for Generative AI

• Students

• 3 mini projects

• 1 major capstone

• GitHub portfolio

• Deployment demo

• Mock interviews

Teaching Method

This program is delivered through live online interactive sessions that combine conceptual teaching with practical coding implementation. The learning approach includes:

• Structured weekly modules with clear progression

• Hands-on coding demonstrations

• Mini-projects for applied learning

• Real industry case studies

• Capstone project mentorship

• Doubt-clearing and personalized guidance

Students also receive guidance on creating a professional AI portfolio and preparing for technical interviews.

Why This Tutor

The course is guided by an experienced AI instructor who focuses on bridging the gap between academic theory and industry application. The teaching style emphasizes clarity, real-world problem solving, and step-by-step learning. Students are supported throughout the program to ensure they build both conceptual understanding and practical confidence.

Benefits & Outcomes

After completing this course, learners will:

• Master core and advanced AI/ML concepts

• Build multiple real-world machine learning projects

• Understand modern generative AI technologies

• Gain deployment and MLOps experience

• Develop a strong AI portfolio for career growth

• Improve readiness for AI/ML job interviews

This program provides a complete pathway from beginner-level knowledge to advanced industry-ready AI expertise.